Quality Index Assessment Research Articles

Multivariate statistical and hydrogeochemical analysis of seasonal groundwater quality variations in coastal villages of Trivandrum district, south India

This study attempts a detailed assessment of the quality of groundwater in the coastal region of Trivandrum District, Kerala where groundwater is the main source of drinking water. Forty groundwater samples were collected during the pre-monsoon and post-monsoon periods. The collected samples were analyzed for physical properties such as electrical conductivity (EC), pH, total dissolved solids (TDS), and total hardness, along with chemical properties, including major cations (Ca2⁺, Mg2⁺, Na⁺, K⁺) and anions (Cl⁻, SO₄2⁻, HCO₃⁻, NO₃⁻). The analysis of groundwater quality reveals significant spatial and seasonal variations caused by both natural and manmade influences. Water Quality Index (WQI), hydrogeochemical plots, and Principal Component Analysis (PCA) were used to analyses the data. The results show that Vakkom, Kazhakottam, Veli-Attipara, and Pozhiyoor show significant deterioration, and areas such as Varkala, Ayroor, and Edava generally maintain good water quality. The Water Quality Index (WQI) assessment indicates that approximately 22.5% of the studied area falls under excellent quality, while 17.5% is classified as poor. The WHO standard and BSI standards were used to derive the WQI. Principal Component Analysis (PCA) identified electrical conductivity, total dissolved solids, and total hardness as the primary factors affecting groundwater quality, explaining 65.17% and 61.03% of the total variance in the pre-monsoon and post-monsoon periods, respectively. Hydrochemical plots collaborate these results, emphasize the influence of rock-water interactions as the main geochemical process, further compounded by pollution from agricultural runoff and urban development. These findings highlight the need for sustainable groundwater management strategies in coastal communities. Effective measures, including pollution mitigation, sustainable agricultural practice, proper waste management, and preservation of freshwater ecosystems, are essential for ensuring the sustainability of groundwater resources.

Appraisal of groundwater suitability for drinking and irrigation utilities in the Cooum River basin, South India: Implications from uranium, nitrate, and fluoride health risks

The study reveals that 6% of groundwater samples exceed the WHO's recommended fluoride limit of 1.5 mg/L, and 15% surpass the nitrate standard of 45 mg/L, rendering the water unsuitable for drinking. Gibbs plot analysis identifies evaporation as the dominant factor affecting groundwater chemistry, while the Piper diagram classifies the water into mixed Ca–Mg–Cl and NaCl types, indicating natural processes and potential seawater intrusion. Cation exchange processes are observed in 75.5% of samples, as shown by Schoeller's indices. The hydrochemical facies evolution suggests freshening from recharge in most samples, with signs of salinity intrusion in others. Between 2010 and 2023, land use and land cover (LULC) changes show a significant increase in built-up areas, resulting in reduced groundwater recharge and heightened pollution risks. The classification accuracy is high, with an overall accuracy of 82.20% and a Kappa coefficient of 83.06%. Water Quality Index (WQI) assessment reveals that 47% of the groundwater samples are excellent for drinking, while 20.6% are suitable. Although 80% of the groundwater remains appropriate for agriculture, the study highlights the need for targeted groundwater management to address urbanization impacts and mitigate health risks from elevated nitrate ingestion, particularly for vulnerable populations such as children.

Key pavement condition indicators for assessing highway pavement maintenance quality index

The technical condition of road pavements requires regular monitoring to support accurate maintenance decisions. However, existing pavement maintenance quality index (PQI) assessment methods are often expensive and infrequent, which limits intelligent decision-making. This paper proposes a cost-effective and precise PQI assessment method using data from five highways in Beijing. Key PQI indicators were identified through a hybrid feature selection method, and a CatBoost model optimised by a genetic algorithm (GA-CatBoost) was developed. The model demonstrated superior accuracy, achieving an R2 of 0.938, an MSE of 0.838, and an MAE of 0.576. It achieved 98.46% accuracy compared to traditional methods, confirming its reliability in an on-site application. This approach offers an economical solution for high-frequency PQI assessment, enabling intelligent decision-making in road infrastructure maintenance and supporting the use of lightweight detection equipment.

Sleep quality and sex-related differences in an older adult population: a web-based cross-sectional study

Introduction: Aging has significant economic and social implications leading to an increase in chronic diseases and reduced autonomy of the elderly. Sleep disorders are common in the elderly, manifesting reduced sleep quality. Objective/Purpose: The study aimed to measure the prevalence of sleep disorders in older adults and assess sex differences in sleep quality and sleep problems. Method: A cross-sectional study based on an online survey was performed according to the STROBE checklist, using pre-validated questionnaires (Pittsburgh Sleep Quality Index) distributed to a sample of older adults. This is the first study evaluating sex differences in sleep quality and problems in the Italian population context using the Pittsburgh Sleep Quality Index assessment tool. The study was conducted in a university hospital in Rome from October 2020 to November 2020, on patients aged >65 years, independent in activities of daily living, with comorbidities and on treatment with up to 2 medications. Results: 59 patients were included in the study, with an average age of 71.12 years. The results show that among males, the prevalence of poor sleep quality was 51.9%, while among females, the prevalence of poor sleep quality was 62.5%. According to Shapiro-Wilk, the Pittsburgh Sleep Quality Index was not normally distributed. Pittsburgh Sleep Quality Index total scores in males did not differ significantly from females. Conclusions: Considering scientific literature, recent studies have highlighted the prevalence of sleep disorders in the elderly population and their underreporting. Providing early intervention not only improves the sleep quality of these people but may also reduce the risk of experiencing adverse health outcomes and susceptibility to frailty.

The impact of sleep disturbances on treatment efficacy and prognosis in patients with depressive and anxiety disorders.

Little was known about the relationship between sleep disturbances and depressive and anxiety disorders, as well as the efficacy of treatment regimens. During 2021 to 2023, a total of 417 participants were screened by Hamilton Depression Rating Scale (HAMD-17) and Hamilton Anxiety Rating Scale (HAMA-14) for psychological status, and Pittsburgh sleep quality index (PSQI) assessment. 409 participants were finally enrolled, of which 188 (45.97%) were suffered from sleep disorders. All participants were received polysomnography (PSG) followed by six-week pharmacological treatment of escitalopram and zopiclone, and finally assessed by HAMD-17 and HAMA-14 for treatment efficacy. PSG monitoring indicated that participants with depression experienced prolonged rapid eye movement sleep latency (REMSL) and increased wakefulness after sleep onset (WASO) (P=0.030 and P=0.002, respectively). Those with anxiety disorders demonstrated a significantly higher percentage of non-rapid eye movement sleep (NREM%) and reduced WASO (P=0.013 and P=0.001, respectively). After six-weeks pharmacological treatment, participants with or without sleep disorders exhibited with similar efficacy outcomes of depression and anxiety disorders (P>0.05). However, every point of PSQI increment at baseline would decrease 0.78 and 0.85 times of probability of effective pharmacological treatment of depression and anxiety disorders. Moreover, participants with both effective outcomes of depression and anxiety disorders were found significant shorter sleep onset latency (SOL) (P<0.001). The insights gained underscore the necessity of considering sleep disturbances in enhancing the overall effectiveness of pharmacological treatments for depression and anxiety disorders.

Appraisal of contamination, hydrogeochemistry, and Monte Carlo simulation of health risks of groundwater in a lithium-rich ore area.

This study incorporated hydrogeochemical facies, the entropy-weighted water quality index (EWQI), multivariate statistics, and probabilistic human exposure assessment to investigate hydrogeochemistry, analyze groundwater quality, and estimate potential risks to human health in a lithium-rich ore area (Jadar River basin, Serbia). The findings designated the Ca·Mg-HCO3 hydrogeochemical type as the predominant type of groundwater, in which rock weathering and evaporation control the major ion chemistry. Due to the weathering of a lithium-rich mineral (Jadarite), the lithium content in the groundwater was very high, up to 567mg/L, with a median value of 4.3mg/L. According to the calculated EWQI, 86.4% of the samples belong to poor and extremely poor quality water for drinking. Geospatial mapping of the studied area uncovered several hotspots of severely contaminated groundwater. The risk assessment results show that groundwater contaminants pose significant non-carcinogenic and carcinogenic human health risks to residents, with most samples exceeding the allowable limits for the hazard index (HI) and the incremental lifetime cancer risk (ILCR). The ingestion exposure pathway has been identified as a critical contaminant route. Monte Carlo risk simulation made apparent that the likelihood of developing cancerous diseases is very high for both age groups. Sensitivity analysis highlighted ingestion rate and human body weight as the two most influential exposure factors on the variability of health risk assessment outcomes.

Fluoride enrichment in groundwater and its association with other chemical ingredients using GIS in the Arjunanadi River basin, Southern India: Implications from improved water quality index and health risk assessment

Fluoride enrichment in groundwater and its association with other chemical ingredients using GIS in the Arjunanadi River basin, Southern India: Implications from improved water quality index and health risk assessment

Hydro-Geochemistry and Water Quality Index Assessment in the Dakhla Oasis, Egypt

Water quality is crucial to the environmental system and thus its chemistry is important, and can be directly related to the water’s source, the climate, and the geology of the region. This study focuses on analyzing the hydrochemistry of specific locations within the Dakhla Oasis in Egypt. A total of thirty-nine groundwater samples representing the Nubian Sandstone Aquifer (NSSA) and seven surface water samples from wastewater lakes and canals were collected for analysis. Key parameters such as pH, electrical conductivity (EC), and total dissolved solids (TDS) were measured on-site, while major ions and trace elements (Fe+2 and Mn+2) were analyzed in the laboratory. The water quality index (WQI) method was employed to assess the overall water quality. Hydro-chemical facies were investigated using Piper’s, Scholler’s, and Stiff diagrams, revealing sodium as the dominant cation and chloride, followed by bicarbonate as the dominant anion. The hydro-chemical composition indicates that Na–Cl constitutes the primary water type in this study. This points to the dissolution of evaporates and salt enrichment due to intense evaporation resulting from the region’s hyper-aridity. In groundwater samples, the order of hydro-chemical facies is HCO3− &gt; Cl− &gt; SO4−2 &gt; Na+ &gt; Ca+2 &gt; K+ &gt; Mg+2, while in wastewater samples, it is Cl− &gt; Na+ &gt; SO4−2 &gt; HCO3− &gt; Ca+2 &gt; Mg+2 &gt; K+. When considering iron and manganese parameters, the water quality index (WQI) values suggest that most groundwater samples exhibit excellent to good quality but become poor or very poor when these elements are included. This study could prove valuable for water resource management in the Dakhla Oasis.

Groundwater Quality Assessment at East El Minia Middle Eocene Carbonate Aquifer: Water Quality Index (WQI) and Health Risk Assessment (HRA)

Around the world, groundwater supply is critical for vital needs such as drinking and irrigation. This work investigates groundwater in the carbonate aquifer of the Middle Miocene in the east El Minia area, Egypt. In this regard, thirty-two groundwater samples were collected. The water samples were analyzed for Ca2+, Mg2+, Na+, K+, Cl−, SO42−, NO3−, CO2−, HCO3−, Fe, Mn, Cd, As, Cr, Cu, and Pb. Groundwater has been evaluated using two methods, which are water quality index (WQI) and health risk assessment (HRA). The predominant groundwater is soft water, and the samples range in salinity from fresh to slightly salty. The groundwater mostly falls into the alkaline water type. All the groundwater samples under study are deemed low quality for human consumption due to water contamination. Fe, Mn, Cd, Cu, and Pb have high HQnc values, which can result in non-carcinogenic health issues in adults, while Mn, Cu, and Pb can give rise to non-carcinogenic health issues in children.

Digital mapping of soil quality and salt-affected soil indicators for sustainable agriculture in the Nile Delta region

The study addresses the challenge of sustainable land management, which is crucial for agricultural production and soil quality (SQ), in the face of land degradation that negatively impacts crop production and SQ. The goal of the current work is to assess SQ using digital soil mapping (DSM) in Kafr El-Sheikh province, Egypt, to develop a framework employing two methods for soil quality index (SQI) assessment: the total data set (SQI-TDS) and a selected minimum data set (SQI-MDS) to choose indicators, along with a weighted additive SQI (SQIw), and a Random Forest (RF) model to predict and map the SQI, as well as the salt-affected soil indicators (EC, pH, and ESP). This framework uses remote sensing data: time series of Sentinel-1 (S-1) and Sentinel-2 (S-2) greenest pixel composite. Additionally, we incorporated environmental covariates derived from S-1 and S-2 imagery to understand their influence on SQ, which in turn informs land management practices, land degradation assessment, and crop productivity. The findings reveal a clear negative impact of salinity and alkalinity on SQ. We demonstrate the importance of Variance Inflation Factor (VIF) and Sequential Feature Selection (SFS) techniques for improving the performance of the RF model used for prediction. Notably, the greenest pixel composite imagery proved promising for SQI assessment using DSM beneath vegetation cover, crop mapping, and land-use dynamics. The precise SQI obtained is essential for decision-makers to detect land degradation, develop sustainable agricultural management strategies, and assess their appropriateness for developing plans and strategies to increase agricultural productivity.

TikTok as a platform for hysteroscopy information: An analytical video-based cross-sectional study to assess quality, reliability, and accuracy.

To assess the quality, reliability, and level of misinformation in TikTok videos about hysteroscopy. A cross-sectional analysis of TikTok videos retrieved using "hysteroscopy" as search term was performed. Patient education materials assessment tool for audio-visual content (PEMAT A/V), the modified DISCERN (mDISCERN), global quality scale (GQS), video information and quality index (VIQI) and misinformation assessment were used. Of three hundred videos captured, 156 were excluded and 144 were included. Most videos were partially accurate or uninformative (43.8% and 34.7%, respectively). Non-healthcare providers produced more inaccurate or uninformative videos than healthcare workers (51.1% vs 4.0%; P < 0.001). Compared to content by professionals, content by patients showed increased distrust towards gynecologists (11.7% vs 0%; P = 0.012) and increased incidence of anxiety and concern towards hysteroscopy (25.5% vs 2%; P < 0.001). PEMAT A/V scores for understandability and actionability were low at 42.9% (interquartile range [IQR]: 11.1-70) and 0% (IQR: 0-0), respectively. Understandability (P < 0.001) and actionability (P = 0.001) were higher for professionals' created content relative to patients' videos. Similarly, median mDISCERN score was low (1 [IQR 0-2]), with significantly higher score for healthcare professionals compared to patients (P < 0.001). Overall video quality was also low, with median VIQI and GQS score of 7 (IQR 4-11) and 1 (IQR 1-3), respectively, and significantly higher scores for healthcare workers' captions compared to patients' for both (P < 0.001 and P = 0.001, respectively). TikTok videos' quality on hysteroscopy seems unsatisfactory and misinformative, with low understandability and actionability scores. Videos recorded by healthcare workers show higher quality and less misinformation than those by patients. Raising the awareness regarding the low quality of medical information on social media is crucial to increase future reliability and trustworthiness.

Implications of Water Quality Index and Multivariate Statistics for Improved Environmental Regulation in the Irtysh River Basin (Kazakhstan)

The selection of sites for permanent environmental monitoring of natural water bodies should rely on corresponding source apportionment studies. Tools like the water quality index (WQI) assessment may support this objective. This study aims to analyze a decade-long dataset of measurements of 26 chemical components at 26 observation points within the Irtysh River Basin, aiming to identify priority zones for stricter environmental regulations. It was achieved through the WQI tool integrated with geoinformation systems (GISs) and multivariate statistical techniques. The findings highlighted that both upstream sections of tributaries (Oba and Bukhtarma rivers) and the mainstream of the basin are generally in good condition, with slight fluctuations observed during flooding periods. Areas in the basin experiencing significant impacts from mining and domestic wastewater treatment activities were identified. The rivers Glubochanka (GL) and Krasnoyarka (KR) consistently experienced marginal water quality throughout the observation period. Various contaminant sources were found to influence water quality. The impact of domestic wastewater treatment facilities was represented by twofold elevated concentrations of chemical oxygen demand, reaching 22.6 and 27.1 mg/L for the KR and GL rivers, respectively. Natural factors were indicated by consistent slight exceedings of recommended calcium levels at the KR and GL rivers. These exceedances were most pronounced during the cold seasons, with an average value equal to 96 mg/L. Mining operations introduced extremal concentrations of trace elements like copper, reaching 0.046–0.051 mg/L, which is higher than the threshold by 12–13 times; zinc, which peaked at 1.57–2.96 mg/L, exceeding the set limit by almost 50–100 times; and cadmium, peaking at levels surpassing 1000 times the safe limit, reaching 0.8 mg/L. The adverse impact of mining activities was evident in the Tikhaya, Ulba, and Breksa rivers, showing similar trends in trace element concentrations. Seasonal effects were also investigated. Ice cover formation during cold seasons led to oxygen depletion and the exclusion of pollutants into the stream when ice melted, worsening water quality. Conversely, flooding events led to contaminant dilution, partially improving the WQI during flood seasons. Principal component analysis and hierarchical cluster analysis indicated that local natural processes, mining activities, and domestic wastewater discharge were the predominant influences on water quality within the study area. These findings can serve as a basis for enhanced environmental regulation in light of updated ecological legislation in Kazakhstan, advocating for the establishment of a comprehensive monitoring network and the reinforcement of requirements governing contaminating activities.

Hydrochemical characterization and health risk assessment of different types of water bodies in Fenghuang Mountain Area, Northeast China.

Groundwater, as an essential resource, holds significant importance for human production and livelihoods. With the deterioration of the water environment, the issue of groundwater quality has become an urgent international concern. This study focused on the Fenghuang Mountain Area (FMA) and collected a total of 41 sets of samples including pore groundwater (PGW), fissure groundwater (FGW), karst groundwater(KGW), and river water(RW). Hydrochemical analysis methods were employed to identify the hydrochemical characteristics and controlling factors. The entropy-weighted water quality index (EWQI) and health risk assessment model were utilized to assess the groundwater quality and nitrate health risk, respectively. The results indicated that the dominant anion and cation in both groundwater and surface water in the FMA were HCO3- and Ca2+, respectively, with the main hydrochemical type being HCO3-Ca. Groundwater and surface water in the FMA were primarily controlled by rock weathering process, with ion concentrations influenced mainly by the dissolution of halite, sylvite, carbonates (calcite and dolomite), silicates, and gypsum, as well as by reverse anion exchange process. PGW was significantly affected by agricultural activities, with NO3- concentration closely related to human activities. The water quality of FGW was relatively good, with Class I and Class II water accounting for the highest proportion, reaching 84.62%. The high-value area of EWQI in PGW was influenced by human activities. The impact of nitrate health risk on children was significantly greater than on adults, with FGW having the lowest health risk and PGW having the highest health risk. The research results can provide important guarantees for the rational development and utilization of water resources in the FMA and the sustainable development of the economy in Northeast China.

Assessment of water quality, heavy metal pollution and human health risks in the Canal system of Ho Chi Minh City, Vietnam

This study was conducted to determine the surface water quality and health risks in Ho Chi Minh City (HCMC) canals. 180 water samples and 180 sediment samples were collected from 15 canal locations in HCMC in 2021 and 2022. The Water Quality Index (WQI) assessment results indicated that the water quality in 2021 ranged from unsuitable to good, with a trend towards improvement in 2022, where good quality water was predominant. TMs PCA/FA identified domestic and agricultural wastewater from HCMC residents as influencing the water quality. Most TMs detected in surface water were within the limits the Vietnamese Ministry of Natural Resources and Environment allowed, except for Pb in 2022 (> 0.02 mg l−1). For sediments, TMs concentrations were higher than in water samples but showed a decreasing trend over the survey period in the order of Hg < Cd < As < Pb < Cu. The findings show that sediments are more strongly affected by TMs than surface water, with the main sources being industrial and agricultural human activities. The non-cancer risk assessment showed that children are more exposed to TMs than adults, mainly through ingestion. Additionally, the cancer risk assessment (CR) identified As in sediments as posing an unacceptable cancer risk (TCR > 1 × 10−4). Therefore, it is necessary to establish high-frequency monitoring policies to analyze and reduce TMs concentrations in water and sediments of the canals to protect human health.

Assessment of hydrochemical characteristics, health risks and quality of groundwater for drinking and irrigation purposes in a mountainous region of Pakistan

Renowned for its agriculture, livestock, and mining, Zhob district, Pakistan, faces the urgent problem of declining groundwater quality due to natural and human-induced factors. This deterioration poses significant challenges for residents who rely on groundwater for drinking, domestic, and irrigation purposes. Therefore, this novel study aimed to carry out a comprehensive assessment of groundwater quality in Zhob district, considering various aspects such as hydrochemical characteristics, human health risks, and suitability for drinking and irrigation purposes. While previous studies may have focused on one or a few of these aspects, this study integrates multiple analyses to provide a holistic understanding of the groundwater quality situation in the region. Additionally, the study applies a range of common hydrochemical analysis methods (acid–base titration, flame atomic absorption spectrometry, and ion chromatography), drinking water quality index (WQI), irrigation indices, and health risk assessment models, using 19 water quality parameters. This multi-method approach enhances the robustness and accuracy of the assessment, providing valuable insights for decision-makers and stakeholders. The results revealed that means of the majority of water quality parameters, such as pH (7.64), electrical conductivity (830.13 μScm–1), total dissolved solids (562.83 mgL–1), as well as various anions, and cations, were in line with drinking water norms. However, the water quality index (WQI) predominantly indicated poor drinking water quality (range = 51–75) at 50% sites, followed by good quality (range = 26–50) at 37% of the sites, with 10% of the sites exhibiting very poor quality (range = 76–100). For irrigation purposes, indices such as sodium percent (mean = 31.37%), sodium adsorption ratio (mean = 0.98 meqL–1), residual sodium carbonate (– 3.15 meqL–1), Kelley’s index (mean = 0.49), and permeability (mean = 49.11%) indicated suitability without immediate treatment. However, the magnesium hazard (mean = 46.11%) and potential salinity (mean = 3.93) demonstrated that prolonged application of groundwater for irrigation needs soil management to avoid soil compaction and salinity. Water samples exhibit characteristics of medium salinity and low alkalinity (C2S1) as well as high salinity and low alkalinity (C3S1) categories. The Gibbs diagram results revealed that rock weathering, including silicate weathering and cation exchange, is the primary factor governing the hydrochemistry of groundwater. The hydrochemical composition is dominated by mixed Ca–Mg–Cl, followed by Na–Cl and Mg–Cl types. Furthermore, the human health risk assessment highlighted that fluoride (F–) posed a higher risk compared with nitrate (NO3–). Additionally, ingestion was found to pose a higher risk to health compared to dermal contact, with children being particularly vulnerable. The average hazard index (HI) for children was 1.24, surpassing the allowable limit of 1, indicating detrimental health effects on this subpopulation. Conversely, average HI values for adult females (0.59) and adult males (0.44) were within safe levels, suggesting minimal concerns for these demographic groups. Overall, the study’s interdisciplinary approach and depth of analysis make a significant contribution to understanding groundwater quality dynamics and associated risks in Zhob district, potentially informing future management and mitigation strategies.

Advanced Machine Learning and Water Quality Index (WQI) Assessment: Evaluating Groundwater Quality at the Yopurga Landfill

As industrial development and population growth continue, water pollution has become increasingly severe, particularly in rapidly industrializing regions like the area surrounding the Yopurga landfill. Ensuring water resource safety and environmental protection necessitates effective water quality monitoring and assessment. This paper explores the application of advanced machine learning technologies and the Water Quality Index (WQI) model as a comprehensive method for accurately assessing groundwater quality near the Yopurga landfill. The methodology involves selecting water quality indicators based on available data and the hydrochemical characteristics of the study area, comparing the performance of Decision Trees, Random Forest, and Xgboost algorithms in predicting water quality, and identifying the optimal algorithm to determine indicator weights. Indicators are scored using appropriate sub-index (SI) functions, and six different aggregation functions are compared to find the most suitable one. The study reveals that the Xgboost model surpasses Decision Trees and Random Forest models in water quality prediction. The top three indicator weights identified are pH, Manganese (Mn), and Nickel (Ni). The SWM model, with a 0% overestimation eclipsing rate and a 34% underestimation eclipsing rate, is chosen as the most appropriate WQI model for evaluating groundwater quality at the Yopurga landfill. According to the WQI results from the SWM aggregation function, the overall water quality in the area ranges from moderately polluted to slightly polluted. These assessment results provide a scientific basis for regional water environment protection.

Air Quality Index Assessment Of An Educational Organization

Abstract Uncontaminated and fresh air is an elementary prerequisite of every living being. But, in this day and age, atmosphere contamination-is certainly the most pervasive kind of environmental-degradation. In the ambience, there are several pollutants which have widespread causing contamination in the air. Pollutants of chief concern include gaseous pollutants like carbon monoxide, ozone, oxides of sulphur and nitrogen, lead etc. These are primary pollutants. Secondary pollutants are particulate matters of different types ranging from fine to high. Every single one of these pollutants can cause a severe menace to human-health. In the current research, sensor-based equipments have been used to monitor and measure the concentration of various pollutants in the air. In the present research work, the effect of different pollutants like PM2.5, PM10, NO2 C02, TVOC, H-CHO, Temperature, Humidity and Noise on air pollution at five stations in the education institute namely RCOEM campus is studied and presented. It had been observed that air pollutants like PM2.5, PM10, and NO2 primarily affected the air quality of RCOEM campus. So, these pollutants are mainly considered to monitor and evaluate air quality of RCOEM campus. After evaluation, air quality index of RCOEM campus was found to be around 94.5. It is within the range of 51-100. So, its falls in the range of satisfactory as per standard norms of central pollution control board and state pollution control board in terms of quality of the air and thereby its contamination.

GIS-based groundwater potential zonation and assessment of groundwater quality and suitability for drinking and irrigation purposes in the Shanmughanadhi river basin, south India

To develop a specific watershed scenario, the quantity and quality of groundwater resources must be assessed and monitored locally. In the present study, an attempt has been made to identify possible groundwater potential zones, determine the quality of groundwater resources in this region, and evaluate their suitability for drinking and irrigation purposes. This goal has been achieved with the combined use of the weighted index overlay analysis (WIOA), groundwater quality index (GWQI), Remote Sensing (RS), and Geographic Information System (GIS) techniques. The proposed study area, Shanmuganadhi, is marked with superior rainfall, oscillating temperature, and runoff with litho-units encompassing charnockite (CHK) and hornblende biotite gneiss (HBG). Over-abstraction of groundwater and intensive agricultural practices have resulted in declining and degrading water quantity and quality in this area. The resulting integrated thematic map has been classified into five groundwater potential zones, namely poor, low, moderate, high, and very high, covering 3.2, 45.49, 15.3, 27.9, and 8.1% area, respectively. Sixty groundwater samples were collected during the pre-monsoon (PRM) and analyzed for major Physico-chemical parameters. From the water quality index assessment, over 28.0% of the samples fall within the “excellent,” 8.0% “good” and 4.0% “poor” in CHK, followed by 5.71% “excellent,” 48.57% “good,” 37.14% “poor,” 5.71% “very poor” and 2.87% “unsuitable” for drinking purposes in HBG terrain of this region. Irrigation indices also demonstrated that most of the groundwater samples in the CHK and HBG exposed zones exhibited good classes for irrigation purposes, as evidenced by the SAR and %Na+. The outcome, which shows the research area's groundwater potential zones, is beneficial for improved groundwater resource planning and management. It is concluded that for larger-scale groundwater-quality investigation and assessment, the groundwater quality index-making approach (GWQI) is more practical and dependable. Water planners and decision-makers may find it helpful in effectively controlling and monitoring groundwater quality at the basin or watershed scale. Reducing urban land use and natural runoff from farms can help prevent water contamination in some regions. To ensure the safe use of groundwater, long-term monitoring of these groundwater was suggested.

Water quality index and health risk assessment for heavy metals in groundwater of Kashiani and Kotalipara upazila, Gopalganj, Bangladesh

The groundwater quality of the Gopalganj district in Bangladesh was not well documented. Therefore, this research was done to determine the groundwater quality in the study area. The water quality index and the human health risk for heavy metal ingestion were used to describe the water quality. The water quality was performed through the estimation of turbidity, pH, electrical conductivity, total dissolved solids, and concentration of sodium, potassium, ammonium, nitrate, iron, manganese, zinc, copper, and arsenic. The mean turbidity, pH, and total dissolved solids in the two upazila were within the permissible limit. However, the electrical conductivity in both the upazila was higher than the WHO-prescribed value in which the higher concentration was in Kotalipara. Excess concentrations of iron and arsenic were found in the Kashiani upazila, but these were below the detection limit in Kotalipara upazila. The water quality index revealed that roughly 61.0% of samples of Kashiani upazila were of poor quality. However, about 96.0% of samples of Kotalipara upazila were of excellent quality. Chronic health risks due to the revelation of drinking have also been determined by assessing the hazard quotient and hazard index. In Kashiani, almost 85.0% of samples were elevated chronic risks for adults and 100.0% of the samples were very high chronic risks for children. In Kotalipara, all the samples (almost 100%) were suggested to have a lower chronic risk for adults and children. The results suggested that the carcinogenic risk of arsenic via oral exposure was very high for both adults and children in Kashiani upazila.

Monitoring the Industrial waste polluted stream - Integrated analytics and machine learning for water quality index assessment

The Water Quality Index (WQI) is a primary metric used to evaluate and categorize surface water quality which plays a crucial role in the management of fresh water resources. Machine Learning (ML) modeling offers potential insights into water quality index prediction. This study employed advanced ML models to get potential insights into the prediction of water quality index for the Aik-Stream, an industrially polluted natural water resource in Pakistan with 19 input water quality variables aligning them with surrounding land use and anthropogenic activities. Six machine learning algorithms, i.e. Adaptive Boosting (AdaBoost), K-Nearest Neighbors (K-NN), Gradient Boosting (GB), Random Forests (RF), Support Vector Regression (SVR), and Bayesian Regression (BR) were employed as benchmark models to predict the Water Quality Index (WQI) values of the polluted stream to achieve our objectives. For model calibration, 80% of the dataset was reserved for training, while 20% was set aside for testing. In our comparative analyses of predictive models for water quality index, the Gradient Boost (GB) model stood out the fittest for its precision, utilizing a combination of just seven parameters (chemical oxygen demand, total organic carbon, oil & grease, Ammonia- nitrogen, arsenic, nickel and zinc), surpassing other models by achieving better results in both training (R2 = 0.88, RMSE = 7.24) and testing (R2 = 0.85, RMSE = 8.67). Analyzing feature importance showed that all the selected variables, except for NO3 N, TDS and temperature had an impact on the accuracy of the models predictions. It is concluded that the application of machine learning to assess water quality in polluted environments enhances accuracy and facilitates real-time tracking, enabling proactive risk mitigations.